In recent years, grid computing has become increasingly popular. In a grid computing environment, multiple users or customers can take advantage of a computer infrastructure that is geographically distributed and redundant. In one implementation, a grid computing environment can include a host that communicates with one or more nodes, each of which can have one or more virtual machines. As a work request is received by the host (e.g., from a customer), it will be allocated to a particular node and then processed by a particular virtual machine. However, as grid computing becomes more popular, various challenges are faced. One such challenge involves the monitoring of grid resource utilization needs and the capturing of resource usage data. Along these lines, it is of particular importance (e.g., for a particular customer or a project) for the amount of CPU utilization expended in processing work loads to be determined. Specifically, if a particular customer utilizes the grid infrastructure for a set of tasks or jobs, the resulting resource usage of the grid infrastructure should be accurately accounted and attributed to the customer.
Heretofore, no existing system provides a way to determine the amount of actual CPU utilized from the perspective of the host. For purposes of measurement for performance as well as for billing, it is desirable to be able to measure a piece of work executed on a virtual machine from the perspective of the host.